# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
# except for the third-party components listed below.
# Hunyuan 3D does not impose any additional limitations beyond what is outlined
# in the repsective licenses of these third-party components.
# Users must comply with all terms and conditions of original licenses of these third-party
# components and must ensure that the usage of the third party components adheres to
# all relevant laws and regulations.

# For avoidance of doubts, Hunyuan 3D means the large language models and
# their software and algorithms, including trained model weights, parameters (including
# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
# fine-tuning enabling code and other elements of the foregoing made publicly available
# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.

import torch
import torch.nn as nn
import math

import torch.nn.functional as F
from diffusers.models.attention import FeedForward


class AddAuxiliaryLoss(torch.autograd.Function):
    """
    The trick function of adding auxiliary (aux) loss,
    which includes the gradient of the aux loss during backpropagation.
    """

    @staticmethod
    def forward(ctx, x, loss):
        assert loss.numel() == 1
        ctx.dtype = loss.dtype
        ctx.required_aux_loss = loss.requires_grad
        return x

    @staticmethod
    def backward(ctx, grad_output):
        grad_loss = None
        if ctx.required_aux_loss:
            grad_loss = torch.ones(1, dtype=ctx.dtype, device=grad_output.device)
        return grad_output, grad_loss


class MoEGate(nn.Module):
    def __init__(
        self, embed_dim, num_experts=16, num_experts_per_tok=2, aux_loss_alpha=0.01
    ):
        super().__init__()
        self.top_k = num_experts_per_tok
        self.n_routed_experts = num_experts

        self.scoring_func = "softmax"
        self.alpha = aux_loss_alpha
        self.seq_aux = False

        # topk selection algorithm
        self.norm_topk_prob = False
        self.gating_dim = embed_dim
        self.weight = nn.Parameter(
            torch.empty((self.n_routed_experts, self.gating_dim))
        )
        self.reset_parameters()

    def reset_parameters(self) -> None:
        import torch.nn.init as init

        init.kaiming_uniform_(self.weight, a=math.sqrt(5))

    def forward(self, hidden_states):
        bsz, seq_len, h = hidden_states.shape
        # print(bsz, seq_len, h)
        ### compute gating score
        hidden_states = hidden_states.view(-1, h)
        logits = F.linear(hidden_states, self.weight, None)
        if self.scoring_func == "softmax":
            scores = logits.softmax(dim=-1)
        else:
            raise NotImplementedError(
                f"insupportable scoring function for MoE gating: {self.scoring_func}"
            )

        ### select top-k experts
        topk_weight, topk_idx = torch.topk(scores, k=self.top_k, dim=-1, sorted=False)

        ### norm gate to sum 1
        if self.top_k > 1 and self.norm_topk_prob:
            denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20
            topk_weight = topk_weight / denominator

        ### expert-level computation auxiliary loss
        if self.training and self.alpha > 0.0:
            scores_for_aux = scores
            aux_topk = self.top_k
            # always compute aux loss based on the naive greedy topk method
            topk_idx_for_aux_loss = topk_idx.view(bsz, -1)
            if self.seq_aux:
                scores_for_seq_aux = scores_for_aux.view(bsz, seq_len, -1)
                ce = torch.zeros(
                    bsz, self.n_routed_experts, device=hidden_states.device
                )
                ce.scatter_add_(
                    1,
                    topk_idx_for_aux_loss,
                    torch.ones(bsz, seq_len * aux_topk, device=hidden_states.device),
                ).div_(seq_len * aux_topk / self.n_routed_experts)
                aux_loss = (ce * scores_for_seq_aux.mean(dim=1)).sum(dim=1).mean()
                aux_loss = aux_loss * self.alpha
            else:
                mask_ce = F.one_hot(
                    topk_idx_for_aux_loss.view(-1), num_classes=self.n_routed_experts
                )
                ce = mask_ce.float().mean(0)
                Pi = scores_for_aux.mean(0)
                fi = ce * self.n_routed_experts
                aux_loss = (Pi * fi).sum() * self.alpha
        else:
            aux_loss = None
        return topk_idx, topk_weight, aux_loss


class MoEBlock(nn.Module):
    def __init__(
        self,
        dim,
        num_experts=8,
        moe_top_k=2,
        activation_fn="gelu",
        dropout=0.0,
        final_dropout=False,
        ff_inner_dim=None,
        ff_bias=True,
    ):
        super().__init__()
        self.moe_top_k = moe_top_k
        self.experts = nn.ModuleList([
            FeedForward(
                dim,
                dropout=dropout,
                activation_fn=activation_fn,
                final_dropout=final_dropout,
                inner_dim=ff_inner_dim,
                bias=ff_bias,
            )
            for i in range(num_experts)
        ])
        self.gate = MoEGate(
            embed_dim=dim, num_experts=num_experts, num_experts_per_tok=moe_top_k
        )

        self.shared_experts = FeedForward(
            dim,
            dropout=dropout,
            activation_fn=activation_fn,
            final_dropout=final_dropout,
            inner_dim=ff_inner_dim,
            bias=ff_bias,
        )

    def initialize_weight(self):
        pass

    def forward(self, hidden_states):
        identity = hidden_states
        orig_shape = hidden_states.shape
        topk_idx, topk_weight, aux_loss = self.gate(hidden_states)

        hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
        flat_topk_idx = topk_idx.view(-1)
        if self.training:
            hidden_states = hidden_states.repeat_interleave(self.moe_top_k, dim=0)
            y = torch.empty_like(hidden_states, dtype=hidden_states.dtype)
            for i, expert in enumerate(self.experts):
                tmp = expert(hidden_states[flat_topk_idx == i])
                y[flat_topk_idx == i] = tmp.to(hidden_states.dtype)
            y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
            y = y.view(*orig_shape)
            y = AddAuxiliaryLoss.apply(y, aux_loss)
        else:
            y = self.moe_infer(
                hidden_states, flat_topk_idx, topk_weight.view(-1, 1)
            ).view(*orig_shape)
        y = y + self.shared_experts(identity)
        return y

    @torch.no_grad()
    def moe_infer(self, x, flat_expert_indices, flat_expert_weights):
        expert_cache = torch.zeros_like(x)
        idxs = flat_expert_indices.argsort()
        tokens_per_expert = flat_expert_indices.bincount().cpu().numpy().cumsum(0)
        token_idxs = idxs // self.moe_top_k
        for i, end_idx in enumerate(tokens_per_expert):
            start_idx = 0 if i == 0 else tokens_per_expert[i - 1]
            if start_idx == end_idx:
                continue
            expert = self.experts[i]
            exp_token_idx = token_idxs[start_idx:end_idx]
            expert_tokens = x[exp_token_idx]
            expert_out = expert(expert_tokens)
            expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]])

            # for fp16 and other dtype
            expert_cache = expert_cache.to(expert_out.dtype)
            expert_cache.scatter_reduce_(
                0,
                exp_token_idx.view(-1, 1).repeat(1, x.shape[-1]),
                expert_out,
                reduce="sum",
            )
        return expert_cache